{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import tensorflow as tf \n",
    "from PIL import Image\n",
    "from nets import nets_factory\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 不同字符数量\n",
    "CHAR_SET_LEN = 10\n",
    "# 图片高度\n",
    "IMAGE_HEIGHT = 60 \n",
    "# 图片宽度\n",
    "IMAGE_WIDTH = 160  \n",
    "# 批次\n",
    "BATCH_SIZE = 1\n",
    "# tfrecord文件存放路径\n",
    "TFRECORD_FILE = \"captcha/test.tfrecords\"\n",
    "\n",
    "# placeholder\n",
    "x = tf.placeholder(tf.float32, [None, 224, 224])  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 从tfrecord读出数据\n",
    "def read_and_decode(filename):\n",
    "    # 根据文件名生成一个队列\n",
    "    filename_queue = tf.train.string_input_producer([filename])\n",
    "    reader = tf.TFRecordReader()\n",
    "    # 返回文件名和文件\n",
    "    _, serialized_example = reader.read(filename_queue)   \n",
    "    features = tf.parse_single_example(serialized_example,\n",
    "                                       features={\n",
    "                                           'image' : tf.FixedLenFeature([], tf.string),\n",
    "                                           'label0': tf.FixedLenFeature([], tf.int64),\n",
    "                                           'label1': tf.FixedLenFeature([], tf.int64),\n",
    "                                           'label2': tf.FixedLenFeature([], tf.int64),\n",
    "                                           'label3': tf.FixedLenFeature([], tf.int64),\n",
    "                                       })\n",
    "    # 获取图片数据\n",
    "    image = tf.decode_raw(features['image'], tf.uint8)\n",
    "    # 没有经过预处理的灰度图\n",
    "    image_raw = tf.reshape(image, [224, 224])\n",
    "    # tf.train.shuffle_batch必须确定shape\n",
    "    image = tf.reshape(image, [224, 224])\n",
    "    # 图片预处理\n",
    "    image = tf.cast(image, tf.float32) / 255.0\n",
    "    image = tf.subtract(image, 0.5)\n",
    "    image = tf.multiply(image, 2.0)\n",
    "    # 获取label\n",
    "    label0 = tf.cast(features['label0'], tf.int32)\n",
    "    label1 = tf.cast(features['label1'], tf.int32)\n",
    "    label2 = tf.cast(features['label2'], tf.int32)\n",
    "    label3 = tf.cast(features['label3'], tf.int32)\n",
    "\n",
    "    return image, image_raw, label0, label1, label2, label3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./captcha/models/crack_captcha.model-6000\n",
      "显示本次图片\n"
     ]
    },
    {
     "data": {
      "image/png": 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kvIfRul7MlxD9OqExvhy6rPHod/WqMqKbSz6+Hu+CbJbVRY6/NckH3v4cH2x+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\nj9cBKHdZeBa4CQ3d9vnVX1X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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2216143d2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "label: [3] [1] [6] [5]\n",
      "predict: [3] [2] [6] [5]\n",
      "显示本次图片\n"
     ]
    },
    {
     "data": {
      "image/png": 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72GyPUGtMvveV5gh/sOVRAP565CYS3WNl8XrRw+lRvoXUkThfPqD8QO9r/0dq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\nQkEQhAj/H5njrux2KQPlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x221614a8208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "label: [7] [3] [0] [3]\n",
      "predict: [7] [3] [0] [3]\n",
      "显示本次图片\n"
     ]
    },
    {
     "data": {
      "image/png": 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EEQqCIEQQoSAIQoRV61NIGTHuTQzyv+z+KQCfn76fESNGTDfipftItb6tP88n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\nQRCECKvap1D0qpHXCzXdHHb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      "text/plain": [
       "<matplotlib.figure.Figure at 0x22166a3d9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "label: [9] [1] [0] [0]\n",
      "predict: [9] [1] [0] [0]\n",
      "显示本次图片\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x22166ac9358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "label: [2] [9] [3] [3]\n",
      "predict: [2] [5] [5] [3]\n",
      "显示本次图片\n"
     ]
    },
    {
     "data": {
      "image/png": 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9llgpHU/UU/e+MwB8pkq9jx+0XY8vns5dw6qqZOwalT4sue/oJR3Ecr5dnmJq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\nEELz/wEh6OAGqJT/AAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22166b1e668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "label: [2] [6] [5] [9]\n",
      "predict: [2] [6] [5] [9]\n",
      "显示本次图片\n"
     ]
    },
    {
     "data": {
      "image/png": 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X9bNm55Z259Kmq5tJVuv84drnAXh/ZJrLKfxyoVws4ZOFuBBx2YWQ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\nQRAEF2IUBEFw8f8BBZCgbBfAPBgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22166b701d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "label: [4] [0] [9] [3]\n",
      "predict: [4] [0] [9] [3]\n",
      "显示本次图片\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x22166b77128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "label: [2] [2] [2] [3]\n",
      "predict: [2] [2] [2] [3]\n",
      "显示本次图片\n"
     ]
    },
    {
     "data": {
      "image/png": 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6V6SasxBiQWRQEEJo3rDhgzi3bn3nhwB44smt5/lOxKlI+CCEWBAZ\nFIQQGhkUhBCaqlhTEEJUD5kpCCE0MigIITQyKAghNDIoCCE0MigIITQyKAghNDIoCCE0MigIITQy\nKAghNDIoCCE0MigIITQyKAghNDIoCCE0MigIITQyKAghNDIoCCE0MigIITQyKAghNDIoCCE0MigI\nITQyKAghNDIoCCE0MigIITT/H+113ZHmjSAHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22166c22780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "label: [9] [1] [4] [4]\n",
      "predict: [9] [1] [4] [4]\n",
      "显示本次图片\n"
     ]
    },
    {
     "data": {
      "image/png": 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papVHWfWBk2z0dwJTd4gq5J4IFe7I1jhY1sRToVao7tSXQgfnHXc2s3tXUy6X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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22166c145c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "label: [1] [3] [5] [1]\n",
      "predict: [1] [3] [5] [1]\n",
      "显示本次图片\n"
     ]
    },
    {
     "data": {
      "image/png": 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w3oj639mYGbvcfcpO6mTmq+/Glup8dPMuTmRVR+6zJTUjU/PAsDxAXctdHUdp\nN6buvBTs8fGBO9jx1EZa7zuj9j9JOHk6pjINqqkOL07kvWIuTEQ0BUEQQohQEAQhhAgFQRBCaJ53\n4bDPK43bu/xdbyLjVqY9nbZdTtoNAPxgZB0vPrsZgIbDLvFhpzxgNdcaYfjePABfvPlJHkxOPjEq\nCF9Vh67G/AlRe84twDpaW54QlTrnhgazalVXFpxX8yA66hAb9lN5Xa/iAzA0xq5Lk31E+TpuXXC8\nfHxfIc2RgVZiz6hQYH13ESOr/Bea6+FFquz+gq3Cj4BTGy1PYTLydtnPMd6ZpvejRVZ2nC+fo+go\nv4fzF20ku4fRsur+5K+fhzmi9hvyV/h7LuOf0/PDsplFScYWqz3l5rvULRtmtEs9m44fuyTOZPFi\nBk4igh1Xxwx+NsvtHd1MxZHR1vIeQT1HgNEVUx4yKdP5C6CSogxXZhjsuwmxzgS9/diMWkmLpiAI\nQggRCoIghBChIAhCiPeMT2EquqwMP8isAeBv//EBGg56pHqVHW7VGAzcoOzR33z4h/xW/V5ajdSU\na1UT+DBO2y5HrNbylORzpbpp8x3K+xprpue5RQAseGkUY2is/J3TUsfYdapEfGyxjlVbuT2RjEZU\nuRuIjbroVeH86LhTbrcWGSmW7XrNdsHx0LMFNMsGV/kE8ivbGF1ikllcWaPuqPpvss8meWyA0Y3K\nbq/bP4Rn+uvlS6F1Qug6TlMNhTY1QSozP4KdUKaslQIzC+ketceaY2PofcN4rouXy+Os7QRgZHly\nWv9A9R4BjM8pn8hs8hSutL9gOibmXUAl3f9S7k18CoIgXBQiFARBCPGeNx9ADVgF+PuhrTzz9dto\nPKTMB6PkMrpEqWfZ+zJ846bHr5gq+VapwGcP/joA3rdaaDikKgdxPLyYweBaZcZoHx9kc9upGa25\nu7+D7MsqXOdGKoNjrTS0vWETP58PdYMauy5N4deG2TLvZPmzINzX93wHxZsy1PxY7aPYoJHqUccm\nhlQqszZhUK06scfYshT2p4cALtj7L84vwn5eVZi2vpFBz1toZ86DpmGtUebU4OdyoT1NpHqPM62q\nnavsKpbKg5CCjuYAg2tMnnjsi2I+CIJw9RGhIAhCiDlTJXk5CZq93pzu5lurbqH2lJKFZs4m5jcD\nGepLctpuZFMsc0X21BmBO+cpN/ozLa2kzqsMSd1yseMGmQ6l6f1O5895rP70jNZ8qdHgd/OPqHV0\nj1+/TjWX/cbbmxk/V4duxdEtt1zBOLxC598se43faaio6rua/ay6x+DpsQ3smbcQgFqzwM92qYa3\ntcdMoqMRdNtvzpL3sP2BOKUWRNcvAAAPS0lEQVQajaFbLP521dMAF2SJ/k3yHF+ufRAAqzaKETMo\nLFuOkXc5+ZBa7wurn+UTqamfQ/Ue4do0G6oJIg5NVKprp2tefLkRTUEQhBAiFARBCCFCQRCEEO8L\nn0JAuzFKQ/sYVkJ1/EkAZlZl12lFgxEnCVwZn0Jaj3NzWlUCfrfpVor16lEEg14CXG/mcntJZJTH\nN3yt/P6Ura4zbtpQrFRpBv+1kx4x3QqtUW2fL2zcCY07Aei24qza1gvA45E7iJ+PYPs9VXVHI9jm\ntjvf4hNNu1hlDgRXWl7vjJ2hq9BCdMQ/znJxYgbZVp32h0/zhQ4VWlwf7Q0dN5Fr3YcwkcB/sOOp\njeXXlzqbcTaIpiAIQggRCoIghBChIAhCiPeVT+GU3cjYeJK2ot8dSFfdjQGiYzpZN3ZF91Ojq65G\nq2/rpvfg0vLnuuOhTd3MeEqWmWlG3IpP4hcZv+pwfxPNhUoqsuvnFLgxj3ojO+V61RWjqqP0AQB+\ntGwV27Yeo9VUlZ2up9MZU5WKnZEhVkeTTOYT6LbTfP/gWlpGK9WVrqmRb9d4sG1fVW7C1P6E9xqb\nYlHw07QffWx7+POrhGgKgiCEEKEgCEIIEQqCIIR4X/gUgs423+vfxIInTXTLz02wXHRd2dexIThR\naOaMfQiAjsjlt2tbfHv+SF8ryVSlqlW3XCJq4BSjTmLG61WX4e54aiOFjWqR9BmN2JiN7ndaLtb4\nI6eaCzQZM8/LaDOUD+SrK78BVGpKwiRD787YGYb8TlTfG76NxP4E8RGVG+HpGtnWCIX5Fsuis5vu\n9F5iruVdiKYgCEIIEQqCIIS4Zs2HLius9i6bVJUNq9S9/3UZqZMj5QEnpSXN5a5B5rjH/zt0I49s\nfR2AjitwZ7ot1YGoMBajYawSMtRcj6j/fsBKM+woM6DBSF64iE9wnUH3nhanhL5LyfyxRR665aH5\n5oPtWyQL24Z9EyY+o/1Obi5Mz5Ab4Q+7HgLg2OEFNA55GAUVknRNnWKDxsc37WGlOcj7KRQ5lxFN\nQRCEECIUBEEIIUJBEIQQ15xPoc9RYbwd+U4+v+9e/uO6HwDQqJ+e0uYOtbtyXEY3zwcg0VsEPyQZ\nzXo4BYN+p8Y/qngZr0Jdx5HCcrWP41Gio36H6ayF5nokB1SY6qVTK3ikQfk5tviRxOphuhV0djy1\nkfZx5X8w+zOc/KTqety830b3Oy87MQMrra65Ppan3biIfOoZcMZWPp8fZdZxtEsNyqk/YJDqtcu/\nKdYb5Ns9Plh7+KL8FcLlQTQFQRBCiFAQBCGECAVBEEJccz6FXkcZ1l84fBd2V5q/iN0NwLIbvkkn\nWbJ+3kGLEVyaXm5x9frfb+DkLzUw/0dB6bBRzlPQbQ88GHODmP3l8ykctzL8rLCYL+/cBkDTOY/Y\nsDqf0XUWNI14XLVWj/ywnj9vvheAG+vOsCJ+jlpdlXh3lVrZFD8BwC/ynSz9aDeZtzoAGNrWQvN+\nZb97vi/CNXWcuIbtu17uaDpGUjMuyzWe8ZMhvvTWHTTsVs8i3WtjFF2KDer96FKdX77nJ6w0+5iY\nHi1cPURTEAQhhAgFQRBCiFAQBCHENedTGPFt/nzBJNWjYQ02AfDFpnvoTA6wd6SD62r6ubd+HwAG\nHilD2evr/vl+Xn9mLY554bRkT4fIgHlFrqHbruM//uwTNPxCnS/da6MVVb6AOzoGhoGRtfzvHE48\nofIZjtatoFQHqz7YBcBAPsUT9gcAeGDhAfYdWQiqzIBVfzeCk4r616bhmTquqTE+P0Jxpar9WJ84\nSVqfWd3DbHmrqHwi2skE6V51bdFRG0/XKDSof4tWP3CURxp2ssK8PHsQLg7RFARBCCFCQRCEENec\n+RCgaarcOT6oTICjX1/JvsZVeDq8fUMzL8eUyp09VYuXUqE5s98knoP4iN95yfVwI0ouFmt07BZr\nVp2IZsOhUo4jlko7/krPbaQPxqg9rUyESM5Bc32Tpq4WIhHshHo0sWGL2HBlnWKDydkTqktzoVmj\nVKuu/1uZTSRPmCx6ekitUyhi9qkDraVtOKaOlTLILfD47I0/AWClOcqlLlc+Y2c4YtXxgz5Vwl1z\nHMzxSip1oclkdJl6/ZGWt1hpGhia/Ns0l5CnIQhCCBEKgiCEmDPmQ9BctZrJGlq26KoKMB6zsBMa\nNWeVCp4Y1LD7dIo1OtrJVDmyEItrxMbVZWqui1GsDFpxIzquqeRiqU7j4zfupTMy6p/p0qjVQYeo\n5zLr+PL3VWZizXGoG3LKHYgA7HrlgddSCxm8IVneY2LYJTZcGQIbG7aIjqqMzPQ5nWK9ykjU30yQ\n7hpF6+33F7RxrlMRADdm4MR0cq06pfYSS2OqSeq7aU478Xmt9CM6R6w6fnv3r2H+XFWb1g455Wdh\nxw1yrTqdm08BYGoOe4o6WyT4MKcQTUEQhBAiFARBCCFCQRCEEHPGp1A9xCSoaqR5+wV+hSZD2ae/\n3LmHJ2vuxDWVfa1bHmbGwcw45a7Fk6G5XtmPUGyIUKhTrzOLXe6p239JOgAdKim/x9Pj6zlZUBmX\nL39/I03HlN2d7LPQbRfP7/rk6RrZdnWdfZvh3tv3kHdUtuOuf1hLnb9u4FsIbHSj6JA8Xxlsg6Gh\nxZWBbnc0ke1QlYqZ+QaFJig1OrTPH2aZ6fsduLghJEHn6KCj1e0P7+bD9fsBeLLvZuzjaVqPqTBw\nJOdQaDLL+xi9wSJWVHv873//MLc/vBtzkucsXD1EUxAEIYQIBUEQQswZ8yEYYtKEVVZLH31s+wW/\nazVSANyUPM7/WZ9juKiacyT6XRKDvirteOi2UtUDFR2U2l39vtCgM7hJHfPH256hMzLETJt9HPdD\njeedBIdL80jqquiq367lSK4dgJef2kxE9ZmlbtQlMVhpWmonIthJJZPzTTrjS9Tnv3/fM3w8fYhx\nV3333X/Wz//93p0AmGMG0VGPaFaZD5GqkKbmgFGMoNWpc+dbTIZXqDXW33uYG2p6aDXHWBc7TZsR\nhBNnp7IHYchg6EwTypzZ+eUNWJ9VodE3TiymaV+4sUtmvnpjbRulLVGk5vMqXBlPqmc92XMWrh6i\nKQiCEEKEgiAIIUQoCIIQYk74FO6dfyPcX3lfDklOw0pzkL+5+Vs8v1L5Ip794WaioybxQY/ouIdh\nKbtbq5jd5Bt0PINyCnG+WePRrcqefSB1dFZpvwf94bC/t/MRnOEY5oiSr1a9S/0+9brtSOECnwYo\nO7tUazC2yA+Hrirxx7eqoTZ3JcP7+FTtblb82jkAjhbm0ZVrYcdP1gDg6RWZ3nBIIzaqYyXU+XLt\nGh/6qLqPjzS9xiozS50eJaaZXEwK97CT47Stqjxf//sNtPSOofuDZ0490Mqur64DoDEPZt5F9+//\n2KII9V3K99C/DdynWrCSFd/KTJ61cGURTUEQhBAiFARBCCFCQRCEEHPCp/BCz17W/uVWQNmYjzYr\nO3+61NelZpqlZpHFEfXbex7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      "text/plain": [
       "<matplotlib.figure.Figure at 0x22166c0ada0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "label: [3] [3] [9] [9]\n",
      "predict: [3] [3] [9] [9]\n",
      "completed\n"
     ]
    }
   ],
   "source": [
    "# 获取图片数据和标签\n",
    "image, image_raw, label0, label1, label2, label3 = read_and_decode(TFRECORD_FILE)\n",
    "\n",
    "#使用shuffle_batch可以随机打乱\n",
    "image_batch, image_raw_batch, label_batch0, label_batch1, label_batch2, label_batch3 = tf.train.shuffle_batch(\n",
    "        [image, image_raw, label0, label1, label2, label3], batch_size = BATCH_SIZE,\n",
    "        capacity = 50000, min_after_dequeue=10000, num_threads=1)\n",
    "\n",
    "#定义网络结构\n",
    "train_network_fn = nets_factory.get_network_fn(\n",
    "    'alexnet_v2',\n",
    "    num_classes=CHAR_SET_LEN,\n",
    "    weight_decay=0.0005,\n",
    "    is_training=False)\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    # inputs: a tensor of size [batch_size, height, width, channels]\n",
    "    X = tf.reshape(x, [BATCH_SIZE, 224, 224, 1])\n",
    "    # 数据输入网络得到输出值\n",
    "    logits0,logits1,logits2,logits3,end_points = train_network_fn(X)\n",
    "    \n",
    "    # 预测值\n",
    "    predict0 = tf.reshape(logits0, [-1, CHAR_SET_LEN])  \n",
    "    predict0 = tf.argmax(predict0, 1)  \n",
    "\n",
    "    predict1 = tf.reshape(logits1, [-1, CHAR_SET_LEN])  \n",
    "    predict1 = tf.argmax(predict1, 1)  \n",
    "\n",
    "    predict2 = tf.reshape(logits2, [-1, CHAR_SET_LEN])  \n",
    "    predict2 = tf.argmax(predict2, 1)  \n",
    "\n",
    "    predict3 = tf.reshape(logits3, [-1, CHAR_SET_LEN])  \n",
    "    predict3 = tf.argmax(predict3, 1)  \n",
    "\n",
    "    # 初始化\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    # 载入训练好的模型\n",
    "    saver = tf.train.Saver()\n",
    "    saver.restore(sess,'./captcha/models/crack_captcha.model-6000')\n",
    "\n",
    "    # 创建一个协调器，管理线程\n",
    "    coord = tf.train.Coordinator()\n",
    "    # 启动QueueRunner, 此时文件名队列已经进队\n",
    "    threads = tf.train.start_queue_runners(sess=sess, coord=coord)\n",
    "\n",
    "    for i in range(10):\n",
    "        # 获取一个批次的数据和标签\n",
    "        b_image, b_image_raw, b_label0, b_label1 ,b_label2 ,b_label3 = sess.run([image_batch, \n",
    "                                                                    image_raw_batch, \n",
    "                                                                    label_batch0, \n",
    "                                                                    label_batch1, \n",
    "                                                                    label_batch2, \n",
    "                                                                    label_batch3])\n",
    "        # 显示图片\n",
    "        print('显示本次图片')\n",
    "        # Image转化为灰度图的时候似乎数据类型等内容有问题，所以直接展示彩图不会报错\n",
    "#         img=Image.fromarray(b_image_raw[0],'L')\n",
    "#         img.show()\n",
    "        plt.imshow(b_image_raw[0])\n",
    "        plt.axis('off')\n",
    "        plt.show()\n",
    "        # 打印标签\n",
    "        print('label:',b_label0, b_label1 ,b_label2 ,b_label3)\n",
    "        # 预测\n",
    "        label0,label1,label2,label3 = sess.run([predict0,predict1,predict2,predict3], feed_dict={x: b_image})\n",
    "        # 打印预测值\n",
    "        print('predict:',label0,label1,label2,label3) \n",
    "                \n",
    "    # 通知其他线程关闭\n",
    "    coord.request_stop()\n",
    "    # 其他所有线程关闭之后，这一函数才能返回\n",
    "    coord.join(threads)\n",
    "print('completed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    " "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
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